Radius Networks Inc.

James Nebeker

David Helms

Marc Wallace

Christopher Sexton

Michael Harper

Scott Yoder

Timothy Judkins

Scott Newman

Techniques for determining an estimated time of arrival associated with a pickup location are described herein. For example, a customer can order a good or service for pickup at a pickup location. In some examples, order data can include a pickup location intent, such as a drive-through location, a pickup window, or a parking location. A location of a user computing device can be determined and can be input to a machine-learning model trained to determine estimated times of arrival for a particular location. In some examples, the machine-learning model can be trained with ground truth data specific to the location for which the model is to determine estimated times of arrival. The machine-learning model can output the estimated time of arrival and can update the time at any regular or irregular intervals, which can be used to prioritize an order in an order queue.

Systems and methods for machine-learning optimization for estimated time of arrival analysis

The disclosure relates to technology for determining a location of a user in a physical venue without need for the user to carry extraneous devices. The method includes installing a background peripheral service (BPS) in a personal mobile device routinely carried by the user where the BPS outputs a temporary ID sequence (TID) when activated. The signal strength of the TID is measured at scanner locations each time the BPS is activated and the measured strengths are used to determine the location of the user. The TID is used to identify an order placed by the user for goods and/or services. This allows a human server to locate the user and deliver the goods and/or services to that user.

Local ephemeral location tracking of mobile device users

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Arlington, VA, United States of America

James Nebeker